Clinical trials have become increasingly complex over the past decade, with factors like the number of endpoints, inclusion-exclusion criteria, and trial duration contributing to this trend. This complexity can lead to increased costs and timelines, as well as a higher risk of trials ending uninformatively.
To address this challenge, researchers have been exploring ways to quantify the complexity of clinical trials. In a recent study, Markey et al. developed a machine learning-based approach to automatically assess key features of trials and create a “Trial Complexity Score.” This score correlates with overall trial duration and can help identify trends in complexity across different therapeutic areas and phases.
Building on these insights, Fast Data Science developed a clinical trial risk assessment tool for the Gates Foundation. This tool leverages Natural Language Processing (NLP) to analyze trial protocols and identify key factors that could impact the complexity of a trial.
After initially developing the tool to cover only HIV and Tuberculosis trials, we are now expanding the scope so that you can use the Clinical Trial Risk Tool to estimate the complexity of a trial in other disease indications such as COVID, Cystic fibrosis, Enteric and diarrheal diseases, Influenza, Malaria, Motor neurone disease, Multiple sclerosis, Neglected tropical diseases, Oncology, and Polio.
As clinical trials continue to evolve, tools like Fast Data Science’s Clinical Trial Risk Tool are essential for navigating the complexities of the process and ensuring that research efforts are efficient, effective, and informative.
